Experiments Overview: Choose the Right Test Type

Documentation Index

Fetch the complete documentation index at: https://docs.abconvert.io/llms.txt

Use this file to discover all available pages before exploring further.

Every ABConvert experiment follows the same workflow—create, preview, launch, analyze, close. What changes is what you’re testing: prices, shipping rates, page templates, images, or checkout elements. This page explains what all experiments have in common, then helps you navigate to the detailed guide for the test type you want to run.

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What every experiment includes

Regardless of which test type you choose, every ABConvert experiment shares these core components:

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1. Hypothesis and primary metric

Before you launch, write a clear hypothesis: “Raising the price from 29 to 34 USD will increase revenue per visitor by 15%.” Then select your primary metric:

• Revenue per visitor — Total revenue divided by total visitors (best for price and offer tests)
• Conversion rate — Orders divided by visitors (best for checkout and template tests)
• Average order value — Total revenue divided by total orders (best for upsell and bundle tests)

ABConvert tracks all metrics regardless of which you choose as primary. The primary metric determines which variant ABConvert flags as the “winner” when you reach statistical significance. See Lifecycle for what each state means and how to preview a variant on your storefront before launch.

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2. Test groups and traffic allocation

Define how many variants you want to test and what percentage of traffic each group receives. Most experiments start with:

• Control (50%) — Your current experience
• Variant (50%) — The change you’re testing

You can add multiple variants (Control, Variant A, Variant B, Variant C) and adjust traffic splits as needed (e.g., 25% each for four groups).

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3. Audience targeting

Control which visitors enter your experiment using filters:

• Country — Restrict experiments to specific markets
• Device type — Desktop only, mobile only, or all devices
• UTM parameters — Target visitors from specific campaigns, sources, or mediums
• Visitor type — New visitors only, returning visitors only, or all

Visitors who don’t match your filters see the default experience and are not counted in experiment analytics. See Audience Targeting for detailed configuration instructions.

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4. Preview mode

Before launching, use Preview to verify the experiment displays correctly on your storefront. Preview applies the variant to your session only—real shoppers still see the original. Preview mode lets you:

• Walk through the buyer journey as if you were a customer
• Switch between variants to test each one
• Catch display issues, app conflicts, or tracking gaps before going live

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5. Statistical significance tracking

ABConvert calculates statistical significance for each variant automatically. The significance indicator shows whether the difference between variants is likely real or just random noise. Aim for:

• 100-200 conversions per variant minimum
• At least 2 weeks of data to account for day-of-week variation
• 95% confidence level before making a final decision

See Statistical Significance for a deep dive into how ABConvert calculates confidence and when to end an experiment.

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Detailed guides for each test type

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Next steps